#### Transcript class10x

```MCB 5472
Bayesian approaches and
decomposition methods
Peter Gogarten
Office: BSP 404
phone: 860 486-4061,
Email: [email protected]
Results midterm:
>100
90-100
80-90
70-80
2
3
4
3
Max
Min
104%
70%
Old Assignment
•Given a multiple fasta sequence file*, write a script that for each sequence extract the
gi number and the species name. and rewrites the file so that the annotation line starts
with the gi number, followed by the species/strain name, followed by a space. (The gi
number and the species name should not be separated by or contain any spaces –
replace them by _. This is useful, because clustalw will recognize the number and
name as handle for the sequence.)
Assume that the annotation line follows the NCBI convention and begins with the
> followed by the gi number, and ends with the species and strain designation given in
[]
Example:
>gi|229240723|ref|ZP_04365119.1| primary replicative DNA
helicase; intein [Cellulomonas flavigena DSM 20109]
Example multiple sequence file is here.
• Work on your student project
More
elegant
less
elegant
More
brute force
Write script from exam
Work of Student project
Elliot Sober’s Gremlins
Observation: Loud noise
in the attic
?
Hypothesis: gremlins in the
attic playing bowling
?
?
Likelihood =
P(noise|gremlins in the attic)
P(gremlins in the attic|noise)
Bayes’ Theorem
Likelihood
describes how
well the model
predicts the
data
P(model|data, I) = P(model, I)
Reverend Thomas Bayes
(1702-1761)
P(data|model, I)
P(data,I)
Posterior
Probability
Prior
Probability
represents the degree
to which we believe a
given model accurately
describes the situation
given the available data
and all of our prior
information I
describes the degree to
which we believe the
model accurately
describes reality
based on all of our prior
information.
Normalizing
constant
Alternative Approaches to Estimate
Posterior Probabilities
Bayesian Posterior Probability Mapping with MrBayes
(Huelsenbeck and Ronquist, 2001)
Problem:
Strimmer’s formula
pi=
Li
L1+L2+L3
only considers 3 trees
(those that maximize the likelihood for
the three topologies)
Solution:
Exploration of the tree space by sampling trees using a biased random walk
(Implemented in MrBayes program)
Trees with higher likelihoods will be sampled more often
pi
Ni
Ntotal
,where Ni - number of sampled trees of topology i, i=1,2,3
Ntotal – total number of sampled trees (has to be large)
Illustration of a biased random walk
Figure generated using MCRobot program (Paul Lewis, 2001)
ml mapping
From: Olga Zhaxybayeva and J Peter Gogarten BMC Genomics 2002, 3:4
ml mapping
Figure 5. Likelihood-mapping analysis for two biological data sets. (Upper) The distribution
patterns. (Lower) The occupancies (in percent) for the seven areas of attraction.
(A) Cytochrome-b data from ref. 14. (B) Ribosomal DNA of major arthropod groups (15).
From: Korbinian Strimmer and Arndt von Haeseler Proc. Natl. Acad. Sci. USA
Vol. 94, pp. 6815-6819, June 1997
(a,b)-(c,d)
/\
/ \
/
\
/
1 \
/ \
/ \
/
\ /
\
/
\/
\
/ 3
:
2 \
/
:
\
/__________________\
(a,d)-(b,c)
(a,c)-(b,d)
Number of quartets in region 1: 68 (= 24.3%)
Number of quartets in region 2: 21 (= 7.5%)
Number of quartets in region 3: 191 (= 68.2%)
Occupancies of the seven areas 1, 2, 3, 4, 5, 6, 7:
Cluster a: 14 sequences
outgroup (prokaryotes)
Cluster b: 20 sequences
other Eukaryotes
Cluster c: 1 sequences
Plasmodium
Cluster d: 1 sequences
Giardia
(a,b)-(c,d)
/\
/ \
/ 1 \
/ \ / \
/
/\
\
/ 6 / \ 4 \
/
/ 7 \
\
/ \ /______\ / \
/ 3 :
5 : 2 \
/__________________\
(a,d)-(b,c)
(a,c)-(b,d)
Number
Number
Number
Number
Number
Number
Number
of
of
of
of
of
of
of
quartets
quartets
quartets
quartets
quartets
quartets
quartets
in
in
in
in
in
in
in
region
region
region
region
region
region
region
1:
2:
3:
4:
5:
6:
7:
53 (= 18.9%)
15 (= 5.4%)
173 (= 61.8%)
3 (= 1.1%)
0 (= 0.0%)
26 (= 9.3%)
10 (= 3.6%)
Decomposition of Phylogenetic Data
Phylogenetic
information
present in
genomes
Break information
into small quanta
of information
(bipartitions or
embedded quartets)
Analyze spectra to
detect transferred
genes and plurality
consensus.
TOOLS TO ANALYZE
PHYLOGENETIC INFORMATION
FROM MULTIPLE GENES IN
GENOMES:
Bipartition Spectra (Lento Plots)
BIPARTITION OF A PHYLOGENETIC TREE
Bipartition (or split) – a division of a
phylogenetic tree into two parts that are
connected by a single branch.
It divides a dataset into two groups, but
it does not consider the relationships
within each of the two groups.
Yellow vs Rest
* * * . . . * *
compatible to illustrated
bipartition
95
* * * . . . . .
Orange vs Rest
. . * . . . . *
incompatible to illustrated
bipartition
“Lento”-plot of 34 supported bipartitions (out of 4082 possible)
13 gammaproteobacterial
genomes
(258 putative
orthologs):
•E.coli
•Buchnera
•Haemophilus
•Pasteurella
•Salmonella
•Yersinia pestis
(2 strains)
•Vibrio
•Xanthomonas
(2 sp.)
•Pseudomonas
•Wigglesworthia
There are
13,749,310,575
possible
unrooted tree
topologies for
13 genomes
Consensus clusters of
eight significantly
supported bipartitions
only 258 genes analyzed
Phylogeny of putatively transferred gene
(virulence factor homologs (mviN))
“Lento”-plot of supported bipartitions (out of 501 possible)
•Anabaena
•Trichodesmium
•Synechocystis sp.
•Prochlorococcus
marinus
(3 strains)
•Marine
Synechococcus
•Thermosynechococcus
elongatus
•Gloeobacter
•Nostoc
punctioforme
Number of datasets
10 cyanobacteria:
Based on 678
sets of
orthologous
genes
Zhaxybayeva, Lapierre and Gogarten, Trends in Genetics, 2004, 20(5): 254-260.
PROBLEMS WITH BIPARTITIONS (A)
Q1={
456
156
256
356
346
146
246
236
136
126
123
124
134
234
235
135
125
145
245
345
345
145
245
235
135
125
123
124
134
234
234
134
124
123
123
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
6
6
6
6
6
6
6
6
6
6
5
5
5
5
4}
7
6
5
4
B1={
**.....,
***....,
****...,
*****..
}
6
5
4
3
3
2
1
1
2
7
bipartitions
embedded
quartets
A single rogue sequence
that moves from one end of
a Hennigian comb to the
other changes all bipartition
B2={
*.....*,
**.....*,
***...*,
****..*
}
Q2={
3456
1456
7456
2456
2356
1356
7356
7256
1256
1756
1726
1736
1236
7236
7246
1246
1746
1346
7346
2346
2345
1345
7345
7245
1245
1745
1725
1735
1235
7235
7234
1234
1734
1724
1 7 2 3}
Decay of bipartition support with number of OTUs
Phylogenies used for simulation
Example for decay of bipartition support with number of OTUs
Sequence
lengths
200
C
D
89
A
C
500
D
74
B
D
A
A
B
C
D
C
D
A
C
B
D
A
A
C
92
A
C
B
A
B
D
B
A
D
86
74
73
B
D
79
91
98
A
D
C
100
B
C
72
100
100
D
94
100
B
C
70
71
99
1000
C
71
A
C
B
D
90
87
87
94
A
B
B
Only branches with better than 70% bootstrap support are shown
Decay of bipartition support with number of OTUs
Each value is the average of 10 simulations using seq-gen.
Simulated sequences were evaluated using PHYML.
Model for simulation and evaluation WAG + Γ(α=1, 4 rate categories)
• The more sequences are added, the
lower the support for bipartitions that
include all sequences. The more data
one uses, the lower the bootstrap
support values become.
• This paradox disappears when only
embedded splits for 4 sequences are
considered.
Bootstrap support values for embedded quartets
+
: tree calculated from one pseudosample generated by bootstraping
from an alignment of one gene
family present in 11 genomes
: embedded quartet for genomes
1, 4, 9, and 10 .
This bootstrap sample supports
the topology ((1,4),9,10).
1
4
9

10
Quartet spectral analyses of genomes iterates
over three loops:
Repeat for all bootstrap samples.
Repeat for all possible embedded quartets.
Repeat for all gene families.
1
10
9
4
1
9
10
4
Boostrap Support Values for
Embedded Quartets
vs.
Bipartitions:
Performance evaluation
using sequence simulations
and phylogenetic
reconstructions
0.01
0.01
N=5(1)
N=4(0)
N=8(4)
0.01
0.01
N=13(9)
0.01
N=23(19)
N=53(49)
Methodology :
Input tree
Seq-Gen
WAG, Cat=4
Alpha=1
Repeat
100 times
Aligned Simulated AA
Sequences (200,500
and 1000 AA)
Extract Highest
Bootstrap support
separating AB><CD
Consense
Extract Bipartitions
For each individual
Count How many trees
trees
embedded quartet
AB><CD is supported
Seqboot
100 Bootstraps
ML Tree Calculation
FastTree, WAG,
Cat=4
Results :
Maximum Bootstrap Support value for
Bipartition separating (AB) and (CD)
Maximum Bootstrap Support value
for embedded Quartet (AB),(CD)
120
100
80
200
60
500
40
1000
20
0
Average Supported Embedded Quartets
Average Maximum Bootstrap Support
120
100
80
200
60
500
40
1000
20
0
0
10
20
30
Number of Interior Branches
40
50
0
10
20
30
Number of interior branches
40
50
Zhaxybayeva and Gogarten, BMC Genomics 2003 4: 37
COMPARISON OF
DIFFERENT SUPPORT
MEASURES
A: mapping of posterior
probabilities according to
Strimmer and von Haeseler
B: mapping of bootstrap
support values
C: mapping of bootstrap
support values from extended
datasets
ml-mapping
versus
bootstrap values from
extended datasets
More gene families group species
according to environment than
according to 16SrRNA phylogeny
In contrast, a themophilic archaeon
has more genes grouping with the
thermophilic bacteria
Quartet decomposition analysis of 19 Prochlorococcus and marine Synechococcus genomes. Quartets
with a very short internal branch or very long external branches as well those resolved by less than
30% of gene families were excluded from the analyses to minimize artifacts of phylogenetic
Evolution occurs within populations where the fittest organisms have a
a population and the population gradually changes.
This reasoning (with many more details) is known as the modern
synthesis.
Note: this is not in contradiction to the the theory of neutral evolution.
(which says what ?)
Processes that MIGHT go beyond inheritance with variation and selection?
•Horizontal gene transfer and recombination
•Polyploidization (botany, vertebrate evolution) see here
•Fusion and cooperation of organisms (Kefir, lichen, also the eukaryotic cell)
•Targeted mutations (?), genetic memory (?) (see Foster's and Hall's reviews on
directed/adaptive mutations; see here for a counterpoint)
•Random genetic drift
•Gratuitous complexity
•Selfish genes (who/what is the subject of evolution??)
•Parasitism, altruism, Morons
•Mutationism, hopeful monsters (see here for a critical discussion by Arlin
Stolzfus)
selection versus drift
see Kent Holsinger’s java simulations at
http://darwin.eeb.uconn.edu/simulations/simulations.html
The law of the gutter.
compare drift versus select + drift
The larger the population the longer it takes for an allele to
become fixed.
Note: Even though an allele conveys a strong selective
advantage of 10%, the allele has a rather large chance to go
extinct.
Note#2: Fixation is faster under selection than under drift.
s=0
Probability of fixation, P, is equal to frequency of allele in population.
Mutation rate (per gene/per unit of time) = u ;
freq. with which allele is generated in diploid population size N: u*2N
Probability of fixation for each allele = 1/(2N)
Substitution rate (the rate with which mutations are fixed in a lineage) =
frequency with which new alleles are generated * Probability of fixation=
u*2N *1/(2N) = u
Therefore:
If f s=0, the substitution rate is independent of population size, and equal
to the mutation rate !!!!
This is the reason that there is hope that the molecular clock might sometimes
work.
Fixation time due to drift alone:
tav=4*Ne generations
(Ne=effective population size; For n discrete generations
Ne= n/(1/N1+1/N2+…..1/Nn)
s>0
Time till fixation on average:
tav= (2/s) ln (2N) generations
(also true for mutations with negative “s” ! discuss among yourselves)
E.g.: N=106,
s=0: average time to fixation: 4*106 generations
s=0.01: average time to fixation: 2900 generations
N=104,
s=0: average time to fixation: 40.000 generations
s=0.01: average time to fixation: 1.900 generations
=> substitution rate of mutation under positive selection is larger
than the rate wite which neutral mutations are fixed.
Random Genetic Drift
Selection
100
Allele frequency
0
Modified from from www.tcd.ie/Genetics/staff/Aoife/GE3026/GE3026_1+2.ppt
Positive selection
• A new allele (mutant) confers some increase in the
fitness of the organism
• Selection acts to favour this allele
• Also called adaptive selection or Darwinian
selection.
NOTE:
Fitness = ability to survive and reproduce
Modified from from www.tcd.ie/Genetics/staff/Aoife/GE3026/GE3026_1+2.ppt
Herbicide resistance gene in nightshade plant
Modified from from www.tcd.ie/Genetics/staff/Aoife/GE3026/GE3026_1+2.ppt
Negative selection
• A new allele (mutant) confers some
decrease in the fitness of the organism
• Selection acts to remove this allele
• Also called purifying selection
Modified from from www.tcd.ie/Genetics/staff/Aoife/GE3026/GE3026_1+2.ppt
Deleterious allele
Human breast cancer gene, BRCA2
5% of breast cancer cases are familial
Mutations in BRCA2 account for 20% of familial cases
Normal (wild type) allele
Mutant allele
(Montreal 440
Family)
Stop codon
4 base pair deletion
Causes frameshift
Modified from from www.tcd.ie/Genetics/staff/Aoife/GE3026/GE3026_1+2.ppt
Neutral mutations
• Invisible to selection (no selection)
• Frequency subject to ‘drift’ in the
population
• Random drift – random changes in small
populations
Types of Mutation-Substitution
• Replacement of one nucleotide by another
• Synonymous (Doesn’t change amino acid)
– Rate sometimes indicated by Ks
– Rate sometimes indicated by ds
• Non-Synonymous (Changes Amino Acid)
– Rate sometimes indicated by Ka
– Rate sometimes indicated by dn
(this and the following 4 slides are from
mentor.lscf.ucsb.edu/course/ spring/eemb102/lecture/Lecture7.ppt)
Genetic Code – Note degeneracy
of 1st vs 2nd vs 3rd position sites
Genetic Code
Four-fold degenerate site – Any substitution is synonymous
From: mentor.lscf.ucsb.edu/course/spring/eemb102/lecture/Lecture7.ppt
Genetic Code
Two-fold degenerate site – Some substitutions synonymous, some
non-synonymous
From: mentor.lscf.ucsb.edu/course/spring/eemb102/lecture/Lecture7.ppt
Measuring Selection on Genes
• Null hypothesis = neutral evolution
• Under neutral evolution, synonymous changes
should accumulate at a rate equal to mutation rate
• Under neutral evolution, amino acid substitutions
should also accumulate at a rate equal to the
mutation rate
From: mentor.lscf.ucsb.edu/course/spring/eemb102/lecture/Lecture7.ppt
Counting #s/#a
Species1
Species2
#s = 2 sites
#a = 1 site
#a/#s=0.5
Ser
TGA
Ser
TGT
Ser
TGC
Ser
TGT
Ser
TGT
Ser
TGT
Ser
TGT
Ser
TGT
Ser
TGT
Ala
GGT
To assess selection pressures one needs to
calculate the rates (Ka, Ks), i.e. the
occurring substitutions as a fraction of the
possible syn. and nonsyn. substitutions.
Things get more complicated, if one wants to take transition
transversion ratios and codon bias into account. See chapter 4 in
Nei and Kumar, Molecular Evolution and Phylogenetics.
Modified from: mentor.lscf.ucsb.edu/course/spring/eemb102/lecture/Lecture7.ppt
dambe
Two programs worked well for me to align nucleotide sequences based
on the amino acid alignment,
One is DAMBE (only for windows). This is a handy program for a lot
of things, including reading a lot of different formats, calculating
phylogenies, it even runs codeml (from PAML) for you.
The procedure is not straight forward, but is well described on the help
pages. After installing DAMBE go to HELP -> general HELP ->
sequences -> align nucleotide sequences based on …->
If you follow the instructions to the letter, it works fine.
DAMBE also calculates Ka and Ks distances from codon based aligned
sequences.
dambe (cont)
aa based nucleotide alignments (cont)
An alternative is the tranalign program that is part of the
emboss package. On bbcxsrv1 you can invoke the program by
typing tranalign.
Instructions and program description are here .
If you want to use your own dataset in the lab on Monday,
generate a codon based alignment with either dambe or
tranalign and save it as a nexus file and as a phylip formated
multiple sequence file (using either clustalw, PAUP (export or
tonexus), dambe, or readseq on the web)
PAML (codeml) the basic model
sites versus branches
You can determine omega for the whole dataset; however,
usually not all sites in a sequence are under selection all the
time.
PAML (and other programs) allow to either determine omega
for each site over the whole tree,
,
or determine omega for each branch for the whole sequence,
.
It would be great to do both, i.e., conclude codon 176 in the
vacuolar ATPases was under positive selection during the
evolution of modern humans – alas, a single site does not
provide any statistics ….
Sites model(s)
work great have been shown to work great in few instances.
The most celebrated case is the influenza virus HA gene.
A talk by Walter Fitch (slides and sound) on the evolution of
this molecule is here .
This article by Yang et al, 2000 gives more background on ml
aproaches to measure omega. The dataset used by Yang et al is
here: flu_data.paup .
sites model in MrBayes
The MrBayes block in a nexus file might look something like this:
begin mrbayes;
set autoclose=yes;
lset nst=2 rates=gamma nucmodel=codon omegavar=Ny98;
mcmcp samplefreq=500 printfreq=500;
mcmc ngen=500000;
sump burnin=50;
sumt burnin=50;
end;
Vincent Daubin and Howard Ochman: Bacterial Genomes
as New Gene Homes: The Genealogy of ORFans in E.
coli. Genome Research 14:1036-1042, 2004
The ratio of nonsynonymous to
synonymous
substitutions for genes
found only in the E.coli Salmonella clade is
lower than 1, but larger
than for more widely
distributed genes.
Fig. 3 from Vincent Daubin and Howard Ochman, Genome Research 14:1036-1042, 2004
Trunk-of-my-car analogy: Hardly anything in there is the is the result
of providing a selective advantage. Some items are removed quickly
(purifying selection), some are useful under some conditions, but
most things do not alter the fitness.
Could some of the inferred purifying selection be due to the acquisition
of novel detrimental characteristics (e.g., protein toxicity)?
MrBayes analyzing the *.nex.p file
1. The easiest is to load the file into excel (if your alignment is
too long, you need to load the data into separate
2. plot LogL to determine which samples to ignore
3. for each codon calculate the the average probability (from
the samples you do not ignore) that the codon belongs to the
group of codons with omega>1.
4. plot this quantity using a bar graph.
plot LogL to determine which samples to ignore
the same after rescaling the y-axis
for each codon calculate the the average probability
copy paste formula
enter formula
plot row
MrBayes on bbcxrv1
If you do this for your own data,
•run the procedure first for only 50000 generations
(takes about 30 minutes) to check that everthing works
as expected,
•then run the program overnight for at least 500 000
generations.
•Especially, if you have a large dataset, do the latter
twice and compare the results for consistency. ( I
prefer two runs over 500000 generations each over one
run over a million generations.)
The preferred wa to run mrbayes is to use the command line:
>mb
Do example on threonlyRS
PAML – codeml – sites model
the paml package contains several distinct programs for nucleotides
(baseml) protein coding sequences and amino acid sequences (codeml)
and to simulate sequences evolution.
The input file needs to be in phylip format.
By default it assumes a sequential format (e.g. here).
If the sequences are interleaved, you need to add an “I” to the first line, as in these
5
855
human
goat-cow
rabbit
rat
marsupial
1
GTG CTG TCT
... ... ...
... ... ...
... ..C ...
... ..C ..G
61
GCT
...
.G.
.G.
..C
GGC
..A
...
..T
..T
GAG
.CT
...
..A
.CC
6
467
gi|1613157 ---------gi|2212798 ---------gi|1564003 MALIQSCSGN
gi|1560076 ---------M
gi|2123365 -----MN--gi|1583936 -----MSQRS
I
MSDNDTIVAQ
MSTTDTIVAQ
TMTTDTIVAQ
QAATETIVAI
-ALPSTIVAI
TKMGDTIAAI
ATPPGRGGVG
ATPPGRGGVG
ATAPGRGGVG
ATAQGRGGVG
ATAAGTGGIG
ATASGAAGIG
ILRISGFKAR
ILRVSGRAAS
IIRVSGPLAA
IVRVSGPLAG
IVRLSGPQSV
IIRLSGSLIK
EVAETVLGKL
EVAHAVLGKL
HVAQTVTGRT
QMAVAVSGRQ
QIAAALGIAG
TIATGLGMTT
LRPRYAEYLP
LKARHAHYGP
LQSRHARYAR
LRPRYAHYTR
FKDVDGSTLD
FTDEDGQQLD
FLDAGGQVID
FRDAQGEVID
FLDVQDEVID
QGIALWFPGP
QGIALYFPGP
QGIALFFPNP
EGLSLYFPGP
DGIAVWFPAP
DGLALWFPAP
NSFTGEDVLE
NSFTGEDVLE
HSFTGEDVLE
NSFTGEDVLE
HSFTGEEVVE
HSFTGEDVLE
LQGHGGPVIL
LQGHGGPVIL
LQGHGGPVVM
LQGHGGPVVL
LQGHGSPVLL
LQGHGSPLLL
I
CCT
G.C
..C
G.A
GA.
GCC
...
..T
.AT
..T
GAC
...
...
...
...
AAG
...
...
..A
...
ACC
T..
...
...
..T
AAC
..T
...
...
C..
GTC
...
A..
A..
..G
AAG
...
...
...
..A
GCC
...
A.T
AA.
...
GCC
...
...
TG.
AT.
TGG
...
...
...
...
GGC
...
.AA
..G
..T
AAG
...
...
...
...
GTT
...
A.C
A..
..G
GGC
...
...
..T
..A
GCG
.GC
AGC
.GC
.GC
CAC
A..
...
..T
...
TAT
...
...
...
..C
GGT
..C
..C
..C
.CA
GCG
..A
..C
.A.
..T
GAG
...
...
...
..A
GCC
..T
...
...
..T
CTG
...
G..
..A
..T
GAG
...
...
C..
.CC
AGG
...
...
...
..A
ATG
...
...
...
.CC
TTC
...
...
...
...
CTG
...
T..
GCT
..C
TCC
AG.
GG.
G..
...
TTC
...
...
...
...
CCC
...
...
...
...
ACC
...
...
...
..T
ACC
...
...
...
...
AAG
...
...
...
..A
PAML – codeml – sites model (cont.)
the program is invoked by typing codeml followed by the name of a control
file that tells the program what to do.
paml can be used to find the maximum likelihood tree, however, the
program is rather slow. Phyml is a better choice to find the tree, which
then can be used as a user tree.
An example for a codeml.ctl file is codeml.hv1.sites.ctl
This file directs codeml to run three different models:
one with an omega fixed at 1, a second where each site can be either have
an omega between 0 and 1, or an omega of 1, and third a model that uses
three omegas as described before for MrBayes.
The output is written into a file called Hv1.sites.codeml_out (as directed by
the control file).
Point out log likelihoods and estimated parameter line (kappa and omegas)
Additional useful information is in the rst file generated by the codeml
Discuss overall result.
PAML – codeml – branch model
For the same dataset to estimate the dN/dS ratios for individual
branches, you could use this file codeml.hv1.branches.ctl as control file.
The output is written, as directed by the control file, into a file called
Hv1.branch.codeml_out
A good way to check for episodes with plenty of non-synonymous
substitutions is to compare the dn and ds trees.
Also, it might be a good idea to repeat the analyses on parts of the
sequence (using the same tree). In this case the sequences encode a family
of spider toxins that include the mature toxin, a propeptide and a signal
Bottom line: one needs plenty of sequences to detect positive selection.
PAML – codeml – branch model
dS -tree
dN -tree
where to get help
read the manuals and help files
check out the discussion boards at http://www.rannala.org/phpBB2/
else
there is a new program on the block called hy-phy
(=hypothesis testing using phylogenetics).
The easiest is probably to run the analyses on the authors datamonkey.
Discussion: Other ways to detect positive selection?
Selective sweep -> fewer alleles present in population
Repeated episodes of positive selection -> high dN
If time discuss http://online.itp.ucsb.edu/online/infobio01/fitch1/
hy-phy
Results of an anaylsis using the SLAC approach
more output might still be here
Hy-Phy
-
Hypothesis Testing using Phylogenies.
Using Batchfiles or GUI
Information at http://www.hyphy.org/
Selected analyses also can be
performed online at
http://www.datamonkey.org/
Example testing for dN/dS in two partitions of the data -John’s dataset
Set up two partitions, define model for each, optimize likelihood
Example testing for dN/dS in two partitions of the data -John’s dataset
Save Likelihood Function
then
select as alternative
The dN/dS ratios for the
two partitions are
different.
Example testing for dN/dS in two partitions of the data -John’s dataset
Set up null
hypothesis, i.e.:
The two dN/dS are
equal
(to do, select both
rows and then click
the define as equal
button on top)
Example testing for dN/dS in two partitions of the data -John’s dataset
Example testing for dN/dS in two partitions of the data -John’s dataset
Name
and
save
as
Nullhyp.
Example testing for dN/dS in two partitions of the data -John’s dataset
After selecting LRT
(= Likelihood Ratio
test), the console
displays the result,
i.e., the beginning
and end of the
sequence alignment
have significantly
different dN/dS
ratios.
Example testing for dN/dS in two partitions of the data -John’s dataset
Alternatively, especially if the the two models are not nested,
one can set up two different windows with the same dataset:
Model 1
Model 2
Example testing for dN/dS in two partitions of the data -John’s dataset
Simulation under model 1, evalutation under model 2, calculate LR
Compare real LR to distribution from simulated LR values. The result might look
something like this
or
this
```